3.3.1: Paper session: Value-based engineering case studies

Interactive dashboards are decision support tools that enable users to explore the relationships between their decisions and the consequences of those decisions. These dashboards in previous research have been proven to be most effective when customized to the specific context of the decision scenario. The objective of this project is to design an interactive dashboard using best practices in optimization and strategic decision-making, for the application of an artillery system derived from publicly available sources. Using Python’s dash library, the resulting dashboard enables users to explore design decisions, model mission success likelihood, optimize the design, explore Pareto-optimal tradeoffs, trace performance improvement goals back to design parameters, and compare designs with existing systems. The dashboard is described in detail, and several use cases are put forward to illustrate the functionality and implementation scenarios for such an interactive dashboard for complex decision analysis. 

The main goal of product line engineering is to build complex system architecture at the best quality, cost, resource ratio. The return on investment in terms of assessment is however not trivial, as system to build rise in complexity. Moreover, the perspective of system of systems engineering that set up both historic and new systems in capabilities, and the introduction of more and more autonomous systems in architectures makes the anticipation of return on investment impossible to achieve without computer assistance. The necessary tools to assess as precisely as possible also involve a wide exploration of possibilities in an ever-changing context. The absence of mechanical or multi physical aspects in SysML-based tools, either in its version 1.3 or 2.0, makes it inefficient in representing or simulating robotic systems or system of system engineering. This article explains the benefits of tackling a classic Multi-Objective Knapsack Problem (MOKP) to the UGV product line items selection using a seamless system architecting toolchain. The association of MBSE (Model-based System Engineering), OR (Operation Research) and MBD (Model Based design) that generated various designs is presented. Our results in system engineering in UGV presents a Pareto Front of trade-offs that can count as numerous possibilities that sole MBSE or separate MBD simulation could not have represented as the best in the sense of . The simultaneous variations in both hardware and mechanical design show was entirely automated using standard tools with no redesign. This shows that seamless automations should pave the future of system engineering tools. With Operation research and Systems engineering tackling methods, our model upscale to real systems will shape System-Driven Engineering that will require new skills in system validation and verification and simulation analytics.

Many complex socio-technical systems enable the conduct of daily activities across the United States. These systems incorporate engineered systems, their human operators, processes, and the people, property, and environments the systems affect. Understanding these socio-technical systems and the interactions within them is difficult. The U.S. Army Corps of Engineers must allocate resources to operate and maintain complex socio-technical systems across multiple business lines, such as Flood Risk Management, in order to mitigate risk. This paper presents a methodology to provide decision makers with improved understanding of their complex socio-technical systems through the development of a risk-informed prioritization framework. Likelihood of facility and system degradation based on the condition of components is developed from subject matter expert initialized Bayesian networks. Designed simulation experiments with hydrological models provide estimates of flood consequences at the watershed level. By combining likelihood and consequence values, this methodology develops relative risk scores that are used as inputs to a mixed integer program that provides decision makers a recommended set of investments given constrained resources. Two case study applications are provided.

Decision-based design often states an aspirational goal for value functions of achieving perfect ordinal consistency. How reliably such a standard can be achieved, however, is rarely addressed. Due to the multiple options available to an engineer regarding model form, model fitting procedures, training data, etc., there are often multiple value functions which could be developed for any particular problem. The extent to which those functions can be distinguished from one another depends on the exact validation procedures used to determine acceptability. This work utilizes a space launch vehicle simulation model to generate outcomes for the comparison of value functions. A training set of 12 outcomes are rank-ordered, and 250 models which produce the correct order of those 12 outcomes are generated. Relative preference of 2 separate alternatives is then compared across all 250 acceptable functions. This comparison is made with both certain and uncertain outcomes associated with the alternatives. In the base condition with certain outcomes, 64% of the models preferred Alternative A and 36% preferred Alternative B. With uncertain outcomes, relative preference depended on both the shape of the resulting distributions as well as the decision criterion used to characterize the distributions. These results demonstrate that functions which produce the same rank-ordering of a training set are not guaranteed to have full ordinal consistency, highlighting the importance of validation procedures in engineering value modelling.